Time relevance and in-frame predication direction based SAO optimization method of HEVC

Abstract

The invention discloses a time relevance and in-frame predication direction based SAO optimization method of HEVC (High efficiency Video Coding). The method includes steps of 1, dividing a frame of image subjected to deblocking smoothing into a plurality of 64*64 LCUs (Largest Coding Units) according to the resolution ratio; 2, processing each LCU block of the divided image and obtaining the LCU model corresponding to a last frame; 3, according to a correspondence relation, excluding an EO mode of the LCU of the current frame or not; 4, obtaining the in-frame predication direction of the LCU block so as to perform classified correspondence; 5, excluding one or two EO modes according to the corresponding in-frame predication direction; 6, performing standard SAO treatment on the rest three modes. The method provided by the invention has beneficial effects that encoding time is saved substantially with no reduction or with slight reduction of coding performance, coding complexity is lowered, real time coding treatment implementation is facilitated and hardware expenditure is also reduced.

Description

technical field [0001] The present invention relates to the technical field of video compression coding, in particular to an HEVC SAO optimization method based on time correlation and intra-frame prediction direction, which can greatly reduce the SAO coding time and facilitate real-time HEVC coding without changing the video image quality . Background technique [0002] Video is a visual, specific and effective information transmission medium. In the video collection process, the original uncompressed video contains a lot of redundant information, which is a very large amount of data. Take the 1080p high-definition video we often see on the Internet as an example, usually the size of a two-hour movie will not exceed 10G. Assuming that the frame rate is 30fps, the data volume of a two-hour uncompressed original video can reach 1920×1080×8bit×3×30fps×7200s=1343.69GB. It is difficult to quickly transmit such a large amount of data with the current network, so The videos we u...

AI Technical Summary

Problems solved by technology

But it also has its own shortcomings: (1) exhaustive calculation of the statistical information of sample data in 8 modes makes the encoding process have a lot of redundant information, which leads to a waste of a lot of encoding time

(2) Too many useless bit streams are written into the encoded file, which reduces the compression efficiency

(3) It is not conducive to the realization of real-time coding

(4) The encoding process is too complicated and requires high hardware requirements, etc.

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